The Analysis of LAPAN-A3/IPB Satellite Image Data Simulation Using High Data Rate Modem

Judianto, Chusnul Tri; Nasser, Eriko Nasemudin

doi:10.1016/j.proenv.2015.03.037

Cited by 14 publications

(6 citation statements)

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“…This section explains the steps of estimating ship velocity based on wake length in which has been obtained in Eq. (25). In general, a sailing ship generates turbulent wake and Kelvin waves, as illustrated in Fig.7.…”

Section: B Velocity Estimation Based On Wake Lengthmentioning

confidence: 99%

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Automatic Ship Recognition Chain on Satellite Multispectral Imagery

et al. 2020

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Section: B Velocity Estimation Based On Wake Lengthmentioning

confidence: 99%

“…This satellite has been injected into polar orbit (98˚ of inclination) at 505 km of altitude in June 2016. One of the imaging payloads mounted to this satellite is a push-broom sensor capable of capturing a 4-band medium resolution MSI with 16m of ground sampling resolution and 122.4 km of the swath width [25] - [27].…”

Section: Introductionmentioning

confidence: 99%

Automatic Ship Recognition Chain on Satellite Multispectral Imagery

et al. 2020

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“…The LAPAN-A3/IPB satellite is the first experimental satellite imagery programme conducted by the Indonesian Institute of Aeronautics and Space. It aims to accelerate the national programme of developing operational imagery satellites (Judianto et al, 2015). Indonesia, which is rich in biodiversity, has abundant marine and land natural resources.…”

Section: Introductionmentioning

confidence: 99%

Application of Lapan A3 Satellite Data for the Identification of Paddy Fields Using Object Based Image Analysis (Obia)

Mukhoriyah

Kushardono

2021

IJReSES

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The role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed.

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“…The LISA sensor is a pushbroom scanner with 15 meter spatial resolution and four bands, ranging from blue visible to near infrared (Zylshal et al 2018). One of the missions of the satellite is to monitor forests and agricultural land (Judianto and Nasser 2015). Consistent reflectance values of the Earth's surface are important in order to perform forest monitoring using Earth Observation (EO) satellite data (Li et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…1. studies focusing on the imaging technology as well as the satellite operational technology (Hasbi and Suhermanto 2013, Hakim et al 2014, Roza et al 2014, Judianto and Nasser 2015,Tahir et al 2016, 2. those focusing on the multispectral image processing (radiometric or spectral characteristics) (Judianto and Nasser 2015, Triharjanto et al 2016, Hakim and Permala 2017, Zylshal et al 2017, Zylshal 2019, 3. studies focusing on multispectral image applications (Nugroho et al 2018, Setiawan et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

Zylshal

2020

Quaestiones Geographicae

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Reflectance variability in mountainous regions caused by steep slopes can decrease the accuracy of landcover mapping. Topographic correction aims to reduce this effect, and various techniques have been proposed to conduct such correction on satellite imagery. This paper presents the initial results of five different topographic correction techniques applied to LAPAN-A3 multispectral images, namely cosine correction, improved cosine correction, Minnaert correction, modified Minnaert correction and two-stage normalization. The widely-available ALOS World 3D 30 meter DEM was employed, with the evaluation made in a mountainous area in South Sulawesi, Indonesia, located in an ancient volcanic region, with slopes ranging from 0 to 60 degrees. The slope aspect was almost equally distributed in all directions. Visual and statistical analysis was conducted before and after the topographic correction to evaluate the results. Standard deviation (SD) and the coefficient variation (CV) were calculated; the results show that the topographic corrections were able to reduce the effect of shadows and relief. Minnaert correction proved to be the best method in terms of visual appearance and spectral variability reduction.

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The Analysis of LAPAN-A3/IPB Satellite Image Data Simulation Using High Data Rate Modem

Cited by 14 publications

References 0 publications

Automatic Ship Recognition Chain on Satellite Multispectral Imagery

Automatic Ship Recognition Chain on Satellite Multispectral Imagery

Application of Lapan A3 Satellite Data for the Identification of Paddy Fields Using Object Based Image Analysis (Obia)

Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

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